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Single-Image Specular Highlight Removal on the Gpu Recent Researches in Applied Informatics Single-Image Specular Highlight Removal On The Gpu COSTIN-ANTON BOIANGIU, RĂZVAN MĂDĂLIN OIŢĂ, MIHAI ZAHARESCU Computer Science Department “Politehnica” University of Bucharest Splaiul Independentei 313, Sector 6, Bucharest, 060042 ROMANIA [email protected], [email protected], [email protected] Abstract: Highlights on textured surfaces are linear combinations of diffuse and specular reflection components. It is sometimes necessary to separate these lighting elements or completely remove the specular light, especially as a preprocessing step for computer vision. Many methods have been proposed for separating the reflection components. The method presented in this article improves on an existing algorithm by porting it on the GPU in order to optimize the speed, using new features found in DirectX11. New test results are also offered. Key-Words: Specular Removal, GPU porting, DirectX11, computer vision 1 Introduction adapting their method to run on a modern GPU. Separation of diffuse and specular lighting This is done using the newest features of the components is an important subject in the field of DirectX11 API, allowing for random read and write computer vision, since many algorithms in the field access from and to buffers containing various types assume perfect diffuse surfaces. For example, a of data. stereo matching algorithm would expect to find similar changes in the positions of the detected regions of interest; however, the specular reflection 2 Related Work actually moves over the surface and doesn’t remain In computer graphics certain models are used to stuck to the same part of the object. Segmentation approximate the intensity distribution of lighting algorithms are highly influenced by the strong and components. Diffuse reflections approximately sharp specular lights; the specular lit object will thus follow Lambert's Law [2], their intensity is manifest two very different regions. Object tracking determined by the angle between the surface normal can also be disturbed by the sudden appearance of a at a point and the vector from the light source. strong specular light on an object that seemed Specular reflections can be modeled based on a constantly lit for a number of frames. 3D object microfacet model, such as the Torrance-Sparrow reconstruction from three images of the same scene model [3], which considers surfaces as consisting of being lit from different angles is very easy to tiny perfect reflectors, averaging their normals using achieve, by calculating the normals from the diffuse a normal distribution function. light intensities, but specular lights again disturb the There are many existing algorithms based on simple light calculations. In conclusion, any image- reflectance, models that separate reflection based classification is influenced by the variations components, but most of them require additional in lighting [7]. images besides the original one (ex: [6]). This is In the real world almost all dielectrics exhibit because the general case of retrieving two intrinsic specular highlights (except those with extremely images from a single one is ill posed, meaning that rough surfaces or a low Fresnel reflectance at we have two unknowns and a single equation. The normal incidence, like chalk), while metals exhibit ability to reconstruct those two images is more or only specular reflection and no diffuse contribution. less the result of an estimation process, but the Thus, it's important to develop algorithms focused helping hand comes from the fact that a color image on removing specular highlights from textured contains not a single channel, but three different surfaces. color channels. Physical properties of the color The algorithm presented in this article improves components for the specular light are different than on an existing algorithm by Tan and Ikeuchi [1], by those of the diffuse light. Two examples are color and polarization: the reflected light is very polarized, ISBN: 978-1-61804-313-9 152 Recent Researches in Applied Informatics meaning that it can be filtered with a polarizing Another advantage is memory speed. Even filter. This technique is used to test the effectiveness though accessing GPU RAM takes orders of of software based solutions. And the most important magnitude more than accessing the GPU Cache part for us, the specular light bounces off the surface (similarly on the CPU), the GPU takes advantage of as soon as it touches it, meaning that it doesn’t its possibility to run millions of threads seemingly in diffuse through the object to be affected by the parallel. When a group of threads is waiting for a object’s color. Thus, the specular reflection has the response from the slower memory, with very little color of the light source, while the diffuse light, that overhead, a new group of threads is started. This is penetrated the object deeper and got scattered by it, like processing a 36x36 tile, and while we are has a mixture of the light source color and the waiting for the data to come, we start a new tile in object’s color. the same processors and put the old threads on wait. In their paper [1], Tan and Ikeuchi introduced an The problem is that mapping the desired problem algorithm based on Shafer's dichromatic reflection to the card architecture is seldom possible. From the model (model explained here: [4]). They used a thousands of threads that can potentially be running single input image, normalized the illumination in parallel, we may end up with hundreds or tens. color using its chromaticity, thus obtaining an image But this also gives a two orders of magnitude with a pure white specular component. By shifting speedup over the CPU, on simple image processing. the intensity and maximum chromaticities of pixels For complex problems, the speedup can become nonlinearly, while retaining their hue a specular- subunitary, as resources are wasted and memory free image was generated. This specular-free image transfer is slow. This paper tries to map a problem has diffuse geometry identical to the normalized on a GPU architecture in order to obtain a real input image, but the surface colors are different. In speedup by massively parallelizing the operations. order to verify if a pixel is diffuse, they used intensity logarithmic differentiation on both the normalized image and the specular-free image. This 4 The Gpu Algorithm diffuse-pixel check is used as a termination The algorithm we decided to port to the GPU is a condition inside an iterative algorithm that removes specularity removal problem, because this is a specular components step by step until no specular widely-used (and sometimes necessary) reflections exist in the image. These processes are preprocessing phase for a lot of computer vision done locally, by sampling a maximum of only two algorithms. We chose an algorithm that works on neighboring pixels. single images, in order to be integrated easily in Most of the presented methods can’t be used in existing applications, is robust, so it doesn’t produce real-time. There are however implementations that unrecognizable results when the input data doesn’t process an image in less than a second, ex: [5]. The fit the requirements, and most importantly, it has the algorithm we chose to implement also has the possibility to be split into multiple individual tasks, drawback of being to slow. in order to be able to benefit from the highly parallel structure of the GPU. We went with Tan and Ikeuchi’s [1] proposed 3 Gpu Processing Overview solution. The authors started from the reflection In the past years, processing on the GPU has started model containing both the specular and diffuse to become widely adopted. This is because the GPU spectral functions and energy diffusion and rewrote has a very different architecture from the CPU, one it in order to find distinguishable characteristics that is very suited for processing images. Image between the two illumination types. The entire processing usually means applying the same technique can be split into two processing identical operations over the entire surface of the approaches: image: on every pixel or on multiple local regions, A single pixel processing step that plots the overlapped or tiled. colors in a different coordinate system in order Modern GPUs have multiple Processing Units to estimate the amount of diffuse and specular (SIMD Cores), the equivalent of a CPU core. Each components. This phase is perfect for Processing unit contains multiple Processing parallelization. elements (Shader Cores). Each shader core can work A phase that restores the specular free images, on vectors of multiple elements. Quantitative, the verifying the amounts of specular reflection of total number of theoretical operations that can be neighboring pixels. This phase is not as easy to done in a cycle is in the order of thousands, where port on the GPU, as neighboring elements need on a modern PC CPU it is around 4. to be accessed simultaneously, and because the ISBN: 978-1-61804-313-9 153 Recent Researches in Applied Informatics processing is done on just one of the neighboring pixels, without priory knowing which is the one that needs to have its specular value lowered. Another limiting factor is that the algorithm is iterative, imposing a strict parallelization limit, but we hope that by speeding up each of the iterations enough, the entire algorithm can gain enough speed. Details regarding the intricacies of the original algorithm can be found in the original paper [1]. Fig. 1 Processing flow. Image from original paper. Some important points will be emphasized in the following paragraphs. Also, advanced applications Given a normalized input image, firstly a of GPGPU based parallelization are described in [5, specular-free image is generated as mentioned 6]. above. Based on these two images, a diffuse All the steps of the algorithm are implemented verification process is run.
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