Beyond Joint Demosaicking and Denoising: An Image Processing Pipeline for a Pixel-bin Image Sensor

• URL: http://arxiv.org/abs/2104.09398v1
• Date: Mon, 19 Apr 2021 15:41:28 GMT
• Title: Beyond Joint Demosaicking and Denoising: An Image Processing Pipeline for a Pixel-bin Image Sensor
• Authors: SMA Sharif, and Rizwan Ali Naqvi, and Mithun Biswas
• Abstract summary: Pixel binning is considered one of the most prominent solutions to tackle the hardware limitation of smartphone cameras. In this paper, we tackle the challenges of joint demosaicing and denoising (JDD) on such an image sensor by introducing a novel learning-based method. The proposed method is guided by a multi-term objective function, including two novel perceptual losses to produce visually plausible images.
• Score: 0.883717274344425
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Pixel binning is considered one of the most prominent solutions to tackle the hardware limitation of smartphone cameras. Despite numerous advantages, such an image sensor has to appropriate an artefact-prone non-Bayer colour filter array (CFA) to enable the binning capability. Contrarily, performing essential image signal processing (ISP) tasks like demosaicking and denoising, explicitly with such CFA patterns, makes the reconstruction process notably complicated. In this paper, we tackle the challenges of joint demosaicing and denoising (JDD) on such an image sensor by introducing a novel learning-based method. The proposed method leverages the depth and spatial attention in a deep network. The proposed network is guided by a multi-term objective function, including two novel perceptual losses to produce visually plausible images. On top of that, we stretch the proposed image processing pipeline to comprehensively reconstruct and enhance the images captured with a smartphone camera, which uses pixel binning techniques. The experimental results illustrate that the proposed method can outperform the existing methods by a noticeable margin in qualitative and quantitative comparisons. Code available: https://github.com/sharif-apu/BJDD_CVPR21.

Related papers

• Multi-scale Frequency Enhancement Network for Blind Image Deblurring [7.198959621445282]
  We propose a multi-scale frequency enhancement network (MFENet) for blind image deblurring. To capture the multi-scale spatial and channel information of blurred images, we introduce a multi-scale feature extraction module (MS-FE) based on depthwise separable convolutions. We demonstrate that the proposed method achieves superior deblurring performance in both visual quality and objective evaluation metrics.
  arXiv  Detail & Related papers  (2024-11-11T11:49:18Z)
• Retinex-RAWMamba: Bridging Demosaicing and Denoising for Low-Light RAW Image Enhancement [71.13353154514418]
  Low-light image enhancement, particularly in cross-domain tasks such as mapping from the raw domain to the sRGB domain, remains a significant challenge. We present a novel Mamba scanning mechanism, called RAWMamba, to effectively handle raw images with different CFAs. We also present a Retinex Decomposition Module (RDM) grounded in Retinex prior, which decouples illumination from reflectance to facilitate more effective denoising and automatic non-linear exposure correction.
  arXiv  Detail & Related papers  (2024-09-11T06:12:03Z)
• Arbitrary-Scale Image Generation and Upsampling using Latent Diffusion Model and Implicit Neural Decoder [29.924160271522354]
  Super-resolution (SR) and image generation are important tasks in computer vision and are widely adopted in real-world applications. Most existing methods, however, generate images only at fixed-scale magnification and suffer from over-smoothing and artifacts. Most relevant work applied Implicit Neural Representation (INR) to the denoising diffusion model to obtain continuous-resolution yet diverse and high-quality SR results. We propose a novel pipeline that can super-resolve an input image or generate from a random noise a novel image at arbitrary scales.
  arXiv  Detail & Related papers  (2024-03-15T12:45:40Z)
• A cross Transformer for image denoising [83.68175077524111]
  We propose a cross Transformer denoising CNN (CTNet) with a serial block (SB), a parallel block (PB), and a residual block (RB) CTNet is superior to some popular denoising methods in terms of real and synthetic image denoising.
  arXiv  Detail & Related papers  (2023-10-16T13:53:19Z)
• Learning Degradation-Independent Representations for Camera ISP Pipelines [14.195578257521934]
  We propose a novel approach to learn degradation-independent representations (DiR) through the refinement of a self-supervised learned baseline representation. The proposed DiR learning technique has remarkable domain generalization capability and it outperforms state-of-the-art methods across various downstream tasks.
  arXiv  Detail & Related papers  (2023-07-03T05:38:28Z)
• Dense Pixel-to-Pixel Harmonization via Continuous Image Representation [22.984119094424056]
  We propose a novel image Harmonization method based on Implicit neural Networks (HINet) Inspired by the Retinex theory, we decouple the harmonizations into two parts to respectively capture the content and environment of composite images. Extensive experiments have demonstrated the effectiveness of our method compared with state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-03-03T02:52:28Z)
• NBD-GAP: Non-Blind Image Deblurring Without Clean Target Images [79.33220095067749]
  Large amounts of blurry-clean image pairs are required for training to achieve good performance. Deep networks often fail to perform well when the blurry images and the blur kernels during testing are very different from the ones used during training.
  arXiv  Detail & Related papers  (2022-09-20T06:21:11Z)
• Del-Net: A Single-Stage Network for Mobile Camera ISP [14.168130234198467]
  Traditional image signal processing (ISP) pipeline in a smartphone camera consists of several image processing steps performed sequentially to reconstruct a high quality sRGB image from the raw sensor data. Deep learning methods using convolutional neural networks (CNN) have become popular in solving many image-related tasks such as image denoising, contrast enhancement, super resolution, deblurring, etc. In this paper we propose DelNet - a single end-to-end deep learning model - to learn the entire ISP pipeline within reasonable complexity for smartphone deployment.
  arXiv  Detail & Related papers  (2021-08-03T16:51:11Z)
• Learning Spatial and Spatio-Temporal Pixel Aggregations for Image and Video Denoising [104.59305271099967]
  We present a pixel aggregation network and learn the pixel sampling and averaging strategies for image denoising. We develop a pixel aggregation network for video denoising to sample pixels across the spatial-temporal space. Our method is able to solve the misalignment issues caused by large motion in dynamic scenes.
  arXiv  Detail & Related papers  (2021-01-26T13:00:46Z)
• Deep Photo Cropper and Enhancer [65.11910918427296]
  We propose a new type of image enhancement problem: to crop an image which is embedded within a photo. We split our proposed approach into two deep networks: deep photo cropper and deep image enhancer. In the photo cropper network, we employ a spatial transformer to extract the embedded image. In the photo enhancer, we employ super-resolution to increase the number of pixels in the embedded image.
  arXiv  Detail & Related papers  (2020-08-03T03:50:20Z)
• High-Resolution Image Inpainting with Iterative Confidence Feedback and Guided Upsampling [122.06593036862611]
  Existing image inpainting methods often produce artifacts when dealing with large holes in real applications. We propose an iterative inpainting method with a feedback mechanism. Experiments show that our method significantly outperforms existing methods in both quantitative and qualitative evaluations.
  arXiv  Detail & Related papers  (2020-05-24T13:23:45Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.