Block-matching and 3D-filtering(BM3D) is a state of the art denoising algorithm for image/video,which takes full advantages of the spatial correlation and the temporal correlation of the video. The algorithm performan...Block-matching and 3D-filtering(BM3D) is a state of the art denoising algorithm for image/video,which takes full advantages of the spatial correlation and the temporal correlation of the video. The algorithm performance comes at the price of more similar blocks finding and filtering which bring high computation and memory access. Area, memory bandwidth and computation are the major bottlenecks to design a feasible architecture because of large frame size and search range. In this paper, we introduce a novel structure to increase data reuse rate and reduce the internal static-random-access-memory(SRAM) memory. Our target is to design a phase alternating line(PAL) or real-time processing chip of BM3 D. We propose an application specific integrated circuit(ASIC) architecture of BM3 D for a 720 × 576 BT656 PAL format. The feature of the chip is with 100 MHz system frequency and a 166-MHz 32-bit double data rate(DDR). When noise is σ = 25, we successfully realize real-time denoising and achieve about 10 d B peak signal to noise ratio(PSNR) advance just by one iteration of the BM3 D algorithm.展开更多
Block matching based 3D filtering methods have achieved great success in image denoising tasks. However the manually set filtering operation could not well describe a good model to transform noisy images to clean imag...Block matching based 3D filtering methods have achieved great success in image denoising tasks. However the manually set filtering operation could not well describe a good model to transform noisy images to clean images. In this paper, we introduce convolutional neural network （CNN） for the 3D filtering step to learn a well fitted model for denoising. With a trainable model, prior knowledge is utilized for better mapping from noisy images to clean images. This block matching and CNN joint model （BMCNN） could denoise images with different sizes and different noise intensity well, especially images with high noise levels. The experimental results demonstrate that among all competing methods, this method achieves the highest peak signal to noise ratio （PSNR） when denoising images with high noise levels （σ 〉 40）, and the best visual quality when denoising images with all the tested noise levels.展开更多
基金the National Natural Science Foundation of China(No.61234001)
文摘Block-matching and 3D-filtering(BM3D) is a state of the art denoising algorithm for image/video,which takes full advantages of the spatial correlation and the temporal correlation of the video. The algorithm performance comes at the price of more similar blocks finding and filtering which bring high computation and memory access. Area, memory bandwidth and computation are the major bottlenecks to design a feasible architecture because of large frame size and search range. In this paper, we introduce a novel structure to increase data reuse rate and reduce the internal static-random-access-memory(SRAM) memory. Our target is to design a phase alternating line(PAL) or real-time processing chip of BM3 D. We propose an application specific integrated circuit(ASIC) architecture of BM3 D for a 720 × 576 BT656 PAL format. The feature of the chip is with 100 MHz system frequency and a 166-MHz 32-bit double data rate(DDR). When noise is σ = 25, we successfully realize real-time denoising and achieve about 10 d B peak signal to noise ratio(PSNR) advance just by one iteration of the BM3 D algorithm.
基金This research was supported by the National Natural Science Foundation of China under Grant Nos. 61573380 and 61672542, and Fundamental Research Funds for the Central Universities of China under Grant No. 2016zzts055.
文摘Block matching based 3D filtering methods have achieved great success in image denoising tasks. However the manually set filtering operation could not well describe a good model to transform noisy images to clean images. In this paper, we introduce convolutional neural network （CNN） for the 3D filtering step to learn a well fitted model for denoising. With a trainable model, prior knowledge is utilized for better mapping from noisy images to clean images. This block matching and CNN joint model （BMCNN） could denoise images with different sizes and different noise intensity well, especially images with high noise levels. The experimental results demonstrate that among all competing methods, this method achieves the highest peak signal to noise ratio （PSNR） when denoising images with high noise levels （σ 〉 40）, and the best visual quality when denoising images with all the tested noise levels.
