Adaptive dualISO HDR-reconstruction
2015 (English)In: EURASIP Journal on Image and Video Processing, ISSN 1687-5176, E-ISSN 1687-5281Article in journal (Refereed) Published
With the development of modern image sensors enabling flexible image acquisition, single shot HDR imaging is becoming increasingly popular. In this work we capture single shot HDR images using an imaging sensor with spatially varying gain/ISO. In comparison to previous single shot HDR capture based on a single sensor, this allows all incoming photons to be used in the imaging, instead of wasting incoming light using spatially varying ND-filters, commonly used in previous works. The main technical contribution in this work is an extension of previous HDR reconstruction approaches for single shot HDR imaging based on local polynomial approximations [15,10]. Using a sensor noise model, these works deploy a statistically informed filtering operation to reconstruct HDR pixel values. However, instead of using a fixed filter size, we introduce two novel algorithms for adaptive filter kernel selection. Unlike previous works, using adaptive filter kernels , our algorithms are based on analysing the model fit and the expected statistical deviation of the estimate based on the sensor noise model. Using an iterative procedure we can then adapt the filter kernel according to the image structure and the statistical image noise. Experimental results show that the proposed filter de-noises the noisy image carefully while well preserving the important image features such as edges and corners, outperforming previous methods. To demonstrate the robustness of our approach, we have exploited input images from raw sensor data using a commercial off-the shelf camera. To further analyze our algorithm, we have also implemented a camera simulator to evaluate different gain pattern and noise properties of the sensor.
Place, publisher, year, edition, pages
Springer Publishing Company, 2015.
HDR reconstruction; Single shot HDR imaging; DualISO; Statistical image fitlering
Computer Science Computer and Information Science
IdentifiersURN: urn:nbn:se:liu:diva-122587DOI: 10.1186/s13640-015-0095-0ISI: 000366324500001OAI: oai:DiVA.org:liu-122587DiVA: diva2:868269
Funding agencies: Swedish Foundation for Strategic Research (SSF) [IIS11-0081]; Linkoping University Center for Industrial Information Technology (CENIIT); Swedish Research Council through the Linnaeus Environment CADICS2015-11-102015-11-102016-01-11Bibliographically approved