341 research outputs found

    Põhjalik uuring ülisuure dünaamilise ulatusega piltide toonivastendamisest koos subjektiivsete testidega

    Get PDF
    A high dynamic range (HDR) image has a very wide range of luminance levels that traditional low dynamic range (LDR) displays cannot visualize. For this reason, HDR images are usually transformed to 8-bit representations, so that the alpha channel for each pixel is used as an exponent value, sometimes referred to as exponential notation [43]. Tone mapping operators (TMOs) are used to transform high dynamic range to low dynamic range domain by compressing pixels so that traditional LDR display can visualize them. The purpose of this thesis is to identify and analyse differences and similarities between the wide range of tone mapping operators that are available in the literature. Each TMO has been analyzed using subjective studies considering different conditions, which include environment, luminance, and colour. Also, several inverse tone mapping operators, HDR mappings with exposure fusion, histogram adjustment, and retinex have been analysed in this study. 19 different TMOs have been examined using a variety of HDR images. Mean opinion score (MOS) is calculated on those selected TMOs by asking the opinion of 25 independent people considering candidates’ age, vision, and colour blindness

    Hardware-based smart camera for recovering high dynamic range video from multiple exposures

    No full text
    International audienceIn many applications such as video surveillance or defect detection, the perception of information related to a scene is limited in areas with strong contrasts. The high dynamic range (HDR) capture technique can deal with these limitations. The proposed method has the advantage of automatically selecting multiple exposure times to make outputs more visible than fixed exposure ones. A real-time hardware implementation of the HDR technique that shows more details both in dark and bright areas of a scene is an important line of research. For this purpose, we built a dedicated smart camera that performs both capturing and HDR video processing from three exposures. What is new in our work is shown through the following points: HDR video capture through multiple exposure control, HDR memory management, HDR frame generation, and rep- resentation under a hardware context. Our camera achieves a real-time HDR video output at 60 fps at 1.3 mega- pixels and demonstrates the efficiency of our technique through an experimental result. Applications of this HDR smart camera include the movie industry, the mass-consumer market, military, automotive industry, and sur- veillanc

    Division Gets Better: Learning Brightness-Aware and Detail-Sensitive Representations for Low-Light Image Enhancement

    Full text link
    Low-light image enhancement strives to improve the contrast, adjust the visibility, and restore the distortion in color and texture. Existing methods usually pay more attention to improving the visibility and contrast via increasing the lightness of low-light images, while disregarding the significance of color and texture restoration for high-quality images. Against above issue, we propose a novel luminance and chrominance dual branch network, termed LCDBNet, for low-light image enhancement, which divides low-light image enhancement into two sub-tasks, e.g., luminance adjustment and chrominance restoration. Specifically, LCDBNet is composed of two branches, namely luminance adjustment network (LAN) and chrominance restoration network (CRN). LAN takes responsibility for learning brightness-aware features leveraging long-range dependency and local attention correlation. While CRN concentrates on learning detail-sensitive features via multi-level wavelet decomposition. Finally, a fusion network is designed to blend their learned features to produce visually impressive images. Extensive experiments conducted on seven benchmark datasets validate the effectiveness of our proposed LCDBNet, and the results manifest that LCDBNet achieves superior performance in terms of multiple reference/non-reference quality evaluators compared to other state-of-the-art competitors. Our code and pretrained model will be available.Comment: 14 pages, 16 figure

    Stereoscopic high dynamic range imaging

    Get PDF
    Two modern technologies show promise to dramatically increase immersion in virtual environments. Stereoscopic imaging captures two images representing the views of both eyes and allows for better depth perception. High dynamic range (HDR) imaging accurately represents real world lighting as opposed to traditional low dynamic range (LDR) imaging. HDR provides a better contrast and more natural looking scenes. The combination of the two technologies in order to gain advantages of both has been, until now, mostly unexplored due to the current limitations in the imaging pipeline. This thesis reviews both fields, proposes stereoscopic high dynamic range (SHDR) imaging pipeline outlining the challenges that need to be resolved to enable SHDR and focuses on capture and compression aspects of that pipeline. The problems of capturing SHDR images that would potentially require two HDR cameras and introduce ghosting, are mitigated by capturing an HDR and LDR pair and using it to generate SHDR images. A detailed user study compared four different methods of generating SHDR images. Results demonstrated that one of the methods may produce images perceptually indistinguishable from the ground truth. Insights obtained while developing static image operators guided the design of SHDR video techniques. Three methods for generating SHDR video from an HDR-LDR video pair are proposed and compared to the ground truth SHDR videos. Results showed little overall error and identified a method with the least error. Once captured, SHDR content needs to be efficiently compressed. Five SHDR compression methods that are backward compatible are presented. The proposed methods can encode SHDR content to little more than that of a traditional single LDR image (18% larger for one method) and the backward compatibility property encourages early adoption of the format. The work presented in this thesis has introduced and advanced capture and compression methods for the adoption of SHDR imaging. In general, this research paves the way for a novel field of SHDR imaging which should lead to improved and more realistic representation of captured scenes

    CNN Injected Transformer for Image Exposure Correction

    Full text link
    Capturing images with incorrect exposure settings fails to deliver a satisfactory visual experience. Only when the exposure is properly set, can the color and details of the images be appropriately preserved. Previous exposure correction methods based on convolutions often produce exposure deviation in images as a consequence of the restricted receptive field of convolutional kernels. This issue arises because convolutions are not capable of capturing long-range dependencies in images accurately. To overcome this challenge, we can apply the Transformer to address the exposure correction problem, leveraging its capability in modeling long-range dependencies to capture global representation. However, solely relying on the window-based Transformer leads to visually disturbing blocking artifacts due to the application of self-attention in small patches. In this paper, we propose a CNN Injected Transformer (CIT) to harness the individual strengths of CNN and Transformer simultaneously. Specifically, we construct the CIT by utilizing a window-based Transformer to exploit the long-range interactions among different regions in the entire image. Within each CIT block, we incorporate a channel attention block (CAB) and a half-instance normalization block (HINB) to assist the window-based self-attention to acquire the global statistics and refine local features. In addition to the hybrid architecture design for exposure correction, we apply a set of carefully formulated loss functions to improve the spatial coherence and rectify potential color deviations. Extensive experiments demonstrate that our image exposure correction method outperforms state-of-the-art approaches in terms of both quantitative and qualitative metrics

    Inverse tone mapping

    Get PDF
    The introduction of High Dynamic Range Imaging in computer graphics has produced a novelty in Imaging that can be compared to the introduction of colour photography or even more. Light can now be captured, stored, processed, and finally visualised without losing information. Moreover, new applications that can exploit physical values of the light have been introduced such as re-lighting of synthetic/real objects, or enhanced visualisation of scenes. However, these new processing and visualisation techniques cannot be applied to movies and pictures that have been produced by photography and cinematography in more than one hundred years. This thesis introduces a general framework for expanding legacy content into High Dynamic Range content. The expansion is achieved avoiding artefacts, producing images suitable for visualisation and re-lighting of synthetic/real objects. Moreover, it is presented a methodology based on psychophysical experiments and computational metrics to measure performances of expansion algorithms. Finally, a compression scheme, inspired by the framework, for High Dynamic Range Textures, is proposed and evaluated
    corecore