A Bayesian Hyperprior Approach for Joint Image Denoising and Interpolation, with an Application to HDR Imaging

Recently, impressive denoising results have been achieved by Bayesian approaches which assume Gaussian models for the image patches. This improvement in performance can be attributed to the use of per-patch models. Unfortunately such an approach is particularly unstable for most inverse problems beyond denoising. In this work, we propose the use of a hyperprior to model image patches, in order to stabilize the estimation procedure. There are two main advantages to the proposed restoration scheme: Firstly it is adapted to diagonal degradation matrices, and in particular to missing data problems (e.g. inpainting of missing pixels or zooming). Secondly it can deal with signal dependent noise models, particularly suited to digital cameras. As such, the scheme is especially adapted to computational photography. In order to illustrate this point, we provide an application to high dynamic range imaging from a single image taken with a modified sensor, which shows the effectiveness of the proposed scheme.Comment: Some figures are reduced to comply with arxiv's size constraints. Full size images are available as HAL technical report hal-01107519v5, IEEE Transactions on Computational Imaging, 201

Reconstruction of high dynamic range images with poisson noise modeling and integrated denoising

In this paper, we present a new method for High Dynamic Range (HDR) reconstruction based on a set of multiple photographs with different exposure times. While most existing techniques take a deterministic approach by assuming that the acquired low dynamic range (LDR) images are noise-free, we explicitly model the photon arrival process by assuming sensor data corrupted by Poisson noise. Taking the noise characteristics of the sensor data into account leads to a more robust way to estimate the non-parametric camera response function (CRF) compared to existing techniques. To further improve the HDR reconstruction, we adopt the split-Bregman framework and use Total Variation for regularization. Experimental results on real camera images and ground-truth data show the effectiveness of the proposed approach

Single Shot High Dynamic Range Imaging Using Piecewise Linear Estimators

International audienceBuilding high dynamic range (HDR) images by combining photographs captured with different exposure times present several drawbacks, such as the need for global alignment and motion estimation in order to avoid ghosting artifacts. The concept of spatially varying pixel exposures (SVE) proposed by Nayar et al. enables to capture in only one shot a very large range of exposures while avoiding these limitations. In this paper, we propose a novel approach to generate HDR images from a single shot acquired with spatially varying pixel exposures. The proposed method makes use of the assumption stating that the distribution of patches in an image is well represented by a Gaussian Mixture Model. Drawing on a precise modeling of the camera acquisition noise, we extend the piecewise linear estimation strategy developed by Yu et al. for image restoration. The proposed method permits to reconstruct an irradiance image by simultaneously estimating saturated and under-exposed pixels and denoising existing ones, showing significant improvements over existing approaches

On the generation of high dynamic range images: theory and practice from a statistical perspective

This dissertation studies the problem of high dynamic range (HDR) image generation from a statistical perspective. A thorough analysis of the camera acquisition process leads to a simplified yet realistic statistical model describing raw pixel values. The analysis and methods then proposed are based on this model. First, the theoretical performance bound of the problem is computed for the static case, where the acquisition conditions are controlled. Furthermore, a new method is proposed that, unlike previous methods, improves the reconstructed HDR image by taking into account the information carried by saturated samples. From a more practical perspective, two methods are proposed to generate HDR images in the more realistic and complex case where both objects and camera may exhibit motion. The first one is a multi-image, patch-based method, that simultaneously estimates and denoises the HDR image. The other is a single image approach that makes use of a general restoration method to generate the HDR image. This general restoration method, applicable to a wide range of problems, constitutes the last contribution of this dissertation

High Dynamic Range Image Reconstruction via Deep Explicit Polynomial Curve Estimation

Due to limited camera capacities, digital images usually have a narrower dynamic illumination range than real-world scene radiance. To resolve this problem, High Dynamic Range (HDR) reconstruction is proposed to recover the dynamic range to better represent real-world scenes. However, due to different physical imaging parameters, the tone-mapping functions between images and real radiance are highly diverse, which makes HDR reconstruction extremely challenging. Existing solutions can not explicitly clarify a corresponding relationship between the tone-mapping function and the generated HDR image, but this relationship is vital when guiding the reconstruction of HDR images. To address this problem, we propose a method to explicitly estimate the tone mapping function and its corresponding HDR image in one network. Firstly, based on the characteristics of the tone mapping function, we construct a model by a polynomial to describe the trend of the tone curve. To fit this curve, we use a learnable network to estimate the coefficients of the polynomial. This curve will be automatically adjusted according to the tone space of the Low Dynamic Range (LDR) image, and reconstruct the real HDR image. Besides, since all current datasets do not provide the corresponding relationship between the tone mapping function and the LDR image, we construct a new dataset with both synthetic and real images. Extensive experiments show that our method generalizes well under different tone-mapping functions and achieves SOTA performance

High Dynamic Range Image Construction and Noise Reduction Using Differently Exposed Images

This thesis discuss how to use mutual information in differently exposed images to improve noise reduction. It also investigates how one can create an high dynamic range (HDR) image from multiple differently exposed images taken with a handheld camera while simultaneously cope with the problems this approach introduces. The proposed method and workflow in this thesis is based on the non-local means algorithm that uses both image registration and a intensity transformation to takes advantage of the mutual information in the different images. It uses alpha expansion to select contiguous areas for the HDR construction and image blending to create seamless transitions between the images. The noise reduction algorithm shows better results with an intensity based noise level compared to a constant one. The methods used to take advantage of the mutual information are proven to be inadequate as the result for using noise reduction in a single image is shown to be just as good. The HDR image construction using both alpha expansion and image blending works well. For smaller movements the approach shows good result and even works as an anti-ghosting algorithm but for larger movements ghosting artifacts are introduced.High­dynamic­range imaging (HDR) är en mängd tekniker som används för att producera bilder med mer detaljer i både mörka och ljusa områden jämfört med ett traditionellt foto. Ett sätt att göra HDR på är att sätta ihop bilder med olika mängd ljus i sig. Problem uppstår dock om kameran rör på sig mellan bilderna eller föremål i bilderna flyttar på sig

LHDR: HDR Reconstruction for Legacy Content using a Lightweight DNN

High dynamic range (HDR) image is widely-used in graphics and photography due to the rich information it contains. Recently the community has started using deep neural network (DNN) to reconstruct standard dynamic range (SDR) images into HDR. Albeit the superiority of current DNN-based methods, their application scenario is still limited: (1) heavy model impedes real-time processing, and (2) inapplicable to legacy SDR content with more degradation types. Therefore, we propose a lightweight DNN-based method trained to tackle legacy SDR. For better design, we reform the problem modeling and emphasize degradation model. Experiments show that our method reached appealing performance with minimal computational cost compared with others.Comment: Accepted in ACCV202