Monte Carlo yolak izlenmiş düşük çözünürlüklü kaplamaların gürültüden arındırılması ve güdümlü yukarı örneklenmesi

Monte Carlo path tracing is used to generate renderings by estimating the rendering equation using the Monte Carlo method. An extensive amount of ray samples per pixel is needed to be cast during this rendering process to create an image with a low enough variance to be considered visually noise-free. Casting that amount of samples requires an expensive time budget. Many studies focus on rendering a noisy image at the original resolution with a decreased sample count and then applying a post-process denoising to produce a visually appealing output. This approach speeds up the rendering process and creates a denoised image of comparable quality to the visually noise-free ground truth. However, the denoising process cannot handle the noisy image’s high variance accurately if the sample count is decreased harshly to complete the rendering process in a shorter time budget. In this thesis work, we try to overcome this problem by proposing a pipeline that renders the image at a reduced resolution to cast more samples than the harshly decreased sample count in the same time budget. This noisy low-resolution image is then denoised more accurately, thanks to having a lower variance. It is then upsampled with the guidance of the auxiliary scene data rendered swiftly in a separate rendering pass at the original resolution. Experimental evaluation shows that the proposed pipeline generates denoised and guided upsampled images in promisingly good quality compared to denoising the noisy original resolution images rendered with the harshly decreased sample count.----M.S. - Master of Scienc

Burst Denoising with Kernel Prediction Networks

We present a technique for jointly denoising bursts of images taken from a handheld camera. In particular, we propose a convolutional neural network architecture for predicting spatially varying kernels that can both align and denoise frames, a synthetic data generation approach based on a realistic noise formation model, and an optimization guided by an annealed loss function to avoid undesirable local minima. Our model matches or outperforms the state-of-the-art across a wide range of noise levels on both real and synthetic data.Comment: To appear in CVPR 2018 (spotlight). Project page: http://people.eecs.berkeley.edu/~bmild/kpn

Pixel-wise Guidance for Utilizing Auxiliary Features in Monte Carlo Denoising

Auxiliary features such as geometric buffers (G-buffers) and path descriptors (P-buffers) have been shown to significantly improve Monte Carlo (MC) denoising. However, recent approaches implicitly learn to exploit auxiliary features for denoising, which could lead to insufficient utilization of each type of auxiliary features. To overcome such an issue, we propose a denoising framework that relies on an explicit pixel-wise guidance for utilizing auxiliary features. First, we train two denoisers, each trained by a different auxiliary feature (i.e., G-buffers or P-buffers). Then we design our ensembling network to obtain per-pixel ensembling weight maps, which represent pixel-wise guidance for which auxiliary feature should be dominant at reconstructing each individual pixel and use them to ensemble the two denoised results of our denosiers. We also propagate our pixel-wise guidance to the denoisers by jointly training the denoisers and the ensembling network, further guiding the denoisers to focus on regions where G-buffers or P-buffers are relatively important for denoising. Our result and show considerable improvement in denoising performance compared to the baseline denoising model using both G-buffers and P-buffers.Comment: 19 page

Image Denoising Using A Generative Adversarial Network

Animation studios render 3D scenes using a technique called path tracing which enables them to create high quality photorealistic frames. Path tracing involves shooting 1000's of rays into a pixel randomly (Monte Carlo) which will then hit the objects in the scene and, based on the reflective property of the object, these rays reflect or refract or get absorbed. The colors returned by these rays are averaged to determine the color of the pixel. This process is repeated for all the pixels. Due to the computational complexity it might take 8-16 hours to render a single frame. We implemented a neural network-based solution to reduce the time it takes to render a frame to less than a second using a generative adversarial network (GAN), once the network is trained. The main idea behind this proposed method is to render the image using a much smaller number of samples per pixel than is normal for path tracing (e.g., 1, 4, or 8 samples instead of, say, 32,000 samples) and then pass the noisy, incompletely rendered image to our network, which is capable of generating a high-quality photorealistic image