Mosaic Super-Resolution Via Sequential Feature Pyramid Networks
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Mosaic Super-resolution via Sequential Feature Pyramid Networks Mehrdad Shoeiby1, Mohammad Ali Armin1, Sadegh Aliakbarian1;2, Saeed Anwar1, Lars Petersson1 1CSIRO-Data61, 2Australian National University [email protected], [email protected] Abstract Advances in the design of multi-spectral cameras have led to great interests in a wide range of applications, from astronomy to autonomous driving. However, such cameras inherently suffer from a trade-off between the spatial and a) b) c) spectral resolution. In this paper, we propose to address this limitation by introducing a novel method to carry out Figure 1. Comparison between the baseline [27] and our Mosaic super-resolution on raw mosaic images, multi-spectral or super-resolution. a) Ground-truth, b) baseline, and c) our mosaic RGB Bayer, captured by modern real-time single-shot mo- super-resolution. The baseline smooths out the bayers pattern and saic sensors. To this end, we design a deep super-resolution fuses the information while our method produces the results simi- architecture that benefits from a sequential feature pyramid lar to the ground-truth along the depth of the network. This, in fact, is achieved reduces the number of possible wavelength channels on the by utilizing a convolutional LSTM (ConvLSTM) to learn the image sensor, and thus creates a limitation in certain ap- inter-dependencies between features at different receptive plications where the size and portability are essential fac- fields. Additionally, by investigating the effect of different tors, for instance, on a UAV [4]. A more portable (i.e. attention mechanisms in our framework, we show that a smaller and lighter) camera suffers more from lower spatial ConvLSTM inspired module is able to provide superior at- and spectral resolution. The spectral and spatial constraints tention in our context. Our extensive experiments and anal- of mosaic imaging devices motivates the need for super- yses evidence that our approach yields significant super- resolution (SR) algorithms for the type of images that these resolution quality, outperforming current state-of-the-art devices create (mosaic images). Nevertheless, compared to mosaic super-resolution methods on both Bayer and multi- the amount of existing literature on normal RGB (interpo- spectral images. Additionally, to the best of our knowledge, lated/demosaiced) RGB images SR, few efforts have been our method is the first specialized method to super-resolve made toward mosaic image super-resolution (SR). mosaic images, whether it be multi-spectral or Bayer. The related literature aiming at the task of SR in RGB 1. Introduction domain (discussed in Section2) is predominantly carried out on interpolated/demosaiced RGB images. However, Real-time snap-shot mosaic image sensors are a category with most modern RGB cameras, mosaic Bayer images arXiv:2004.06853v1 [eess.IV] 15 Apr 2020 of imaging devices that encompass modern RGB and multi- can be obtained instead of demosaiced images. In this re- spectral cameras. In fact, RGB cameras are a sub-category gard, various studies [40,8] have pointed out and discussed of multi-spectral cameras, only being capable of measur- that interpolation or demosaicing deteriorates SR perfor- ing three spectral channels i.e. red, blue and green. The re- mance due to (1) removing high-frequency information as cent improvements has given rise to multi-spectral cameras interpolation/demosaicing can be viewed as a form of low with the performance comparable to modern RGB cameras pass filtering [14], while SR aims at predicting such high- in terms of size, portability and speed [34]. frequency information; and (2) interpolation/demosaicing Despite the great interest in these devices, with applica- introduces artifacts [40, 14] which can be either viewed as a tions ranging from astronomy [2] to tracking in autonomous signal loss or noise in the input image. The SR literature on vehicles [31, 24], they suffer from an inherent constraint: mosaic RGB images, despite its importance, is limited to a a trade-off between the spatial and the spectral resolution. few recent works [18, 29, 37]. We believe that in most mod- The reason is the limited physical space on 2D camera im- ern SR applications such as microscopy and astronomy, in age sensors. A higher spatial resolution (smaller pixel size) which having access to high-resolution images is vital, it is 1 counter-intuitive to throw away high-frequency content mo- the raw mosaic images instead should result in better perfor- saic images and only rely on the interpolated/demosaiced mance. images. Current approaches, similar to ours, use residual connec- Surprisingly, despite its significance, mosaic image SR tions [15, 22,1]. For example, [15] introduced very deep SR has been less considered in the literature. This, however, (VDSR), which has a single global skip-connection from motivates us to conduct an in-depth study on how to get ben- the input to the final output. Similarly, enhanced deep SR efit of such vital information. Therefore, in this paper, we i.e. EDSR [22] employs residual blocks (RBs) with short propose a SR framework for real-time mosaic image sensors skip connections. More recently, a cascading residual net- (cameras) to bridge the gap identified in the literature. We work (CARN) [1] is proposed, which also employs a vari- believe that our approach will benefit many applications, ant of RBs with cascading connections. While CARN [1] is such as in astronomy [21] or microscopy [25] in which high lagging behind EDSR [22] in terms of PSNR, it improves quality super-resolved images are essential. To summarize, efficiency and speed. More lately, motivated by the suc- our primary contributions are: cess of DenseNet [13], CNN-based SR networks have con- centrated on the dense connection model. For example, • We demonstrate an effective use of the ConvLSTM SRDenseNet [32] uses dense connections to learn compact module to learn the relationship between features from models, avoiding the problem of vanishing gradients and different stages of a CNN with sequentially increasing eases the flow from low-level features to high-level fea- receptive fields, and achieve state-of-the-art in mosaic tures. Recently, the residual-dense network (RDN) [39] SR. employed dense connections to learn the local representa- • To the best of our knowledge, our method is the tions from the patches at hand. The dense network with first addressing SR of mosaic images directly. We multi-scale skip connections has similarities with our pro- also demonstrate the different nature of mosaic im- posed method in terms of feature aggregation. However, ages compared to demosaiced images by showing that their method aggregates features, whereas our method ag- methods specifically designed for mosaic SR does not gregates a sequence of features with sequentially increasing necessarily perform well on demosaiced/interpolated receptive fields, and uses a ConvLSTM module [35] to learn RGB images. these sequential features. The visual attention [23] concept in SR was introduced As a secondary contribution, we experiment with dif- by RCAN [37], which models the inter-channel depen- ferent attention mechanisms and assess their relative per- dencies using a channel attention (CA) mechanism. This formance in our network. Furthermore, investigating the process is coupled with a very deep network composed structure of an LSTM module, we discover that elements of groups of RBs called RGs (RGs). Following in the of it are designed to apply implicit attention. By incorporat- footsteps of [37], the residual attention module (SRRAM) ing our LSTM inspired attention mechanism in our network, by [16], employed spatial attention as well as CA while still we empirically show its superior performance compared to lagging behind RCAN [37]. Most recently, Second-Order other attention mechanisms. Attention Network (SAN) [3] was introduced. The authors argue that the task of SR has achieved outstanding perfor- 2. Related Work mance; however, at the expense of using very deep and wide The RGB super-resolution methods dominate the SR lit- networks. They argue that maybe a better utilization of in- erature; therefore, we first review the literature that focus termediate features would help improve the results. Note on RGB SR and then discuss the more related existing lit- that better utilization of intermediate features was brought erature on mosaic SR. One of the first works in RGB CNN up by RCAN; in fact, this was precisely the incentive be- based SR (SRCNN) [5], although simply composed of three hind CA in the RCAN setup. Nevertheless, the SAN au- convolutional layers, significantly outperformed the con- thors [3] propose a second-order attention network within a ventional SR algorithms. Following the success of SR- residual in residual structure. The quantitative results are, CNN [5], many CNN based algorithms [22,6, 36] were on average, on par with RCAN, and in terms of the network developed. For example, fast SRCNN (FSRCNN) [6] en- size, SAN is only marginally smaller than RCAN (15.7M compassing eight convolutional layers, speeds up the SR vs. 16M parameters). process by using as input the original low-resolution patch The SR algorithms above focus mainly on super- instead of a bi-cubically upsampled one. They highlight the resolving RGB images even though, as discussed before, fact that using interpolation to scale up images deteriorates the multi-spectral images are comparatively more adversely the SR performance. Note that, extending from the discus- affected by the resolution constraints. Reviewing the lim- sion in [6], RGB images are, in fact, interpolated from mo- ited multi-spectral/mosaic SR literature, one would realize saic Bayer images, and hence, super-resolving directly from that these networks are not structurally different i.e. they are not fine-tuned for mosaic images by taking into account representation, processing them in a pyramid Bidirectional any spectral correlation of different channels.