Abstract
A novel strategy is proposed to address block artifacts in a conventional dark channel prior (DCP). The DCP was used to estimate the transmission map based on patch-based processing, which also results in image blurring. To enhance a degraded image, the proposed single-image dehazing technique restores a blurred image with a refined DCP based on a hidden Markov random field. Therefore, the proposed algorithm estimates a refined transmission map that can reduce the block artifacts and improve the image clarity without explicit guided filters. Experiments were performed on the remote-sensing images. The results confirm that the proposed algorithm is superior to the conventional approaches to image haze removal. Moreover, the proposed algorithm is suitable for image matching based on local feature extraction.
Highlights
Image matching is inevitably challenging when different images of a large scene are being matched
Local feature points are generally used to match the same location in different images, image matching is difficult in terms of viewpoint, illumination, and temporal changes
The transmission map of dark channel prior (DCP) is refined using the hidden Markov random field and expectation maximization (HMRF-EM) algorithm, which is explained in Sections 2.1 and 2.2
Summary
Image matching is inevitably challenging when different images of a large scene are being matched. Conventional methods for removing haze use additional hardware [26,27,28,29,30,31], multiple images [32], or additional information [33]. Multiple-image-based dehazing methods remove haze through two or more input images These methods were developed by Tan [34], Appl. These methods were developed by Tan wibHboc[eTaaiunonae3uvnisaadmcs4gtwseedruheai]dersd,pduthomc,lciieousioepnoboacepernrtrnenhntewntiehtacthndheaihn[naohn.c3riuatydmsHiis5Aupczsa]agboheaeewnanppaeudulcahitpiosrissutchtlaaoeimrhyodterinzdopissoemreeeaoieemufiopstacptsirnlthpst[eottioah3oceapdarvlin5a.res.nree;mAe]lam[oyndnhn3ipanfneoho6otgabcrdcft]wavasoheoyuianrtenoparetemidcvmiamnpovsceeilhseeabsyfaentehrinyixytst,ladboirattsiitolelsmiitmizh.ylccanriet[aiaa;tiaos3pzylfolhix6pammirremoiinr]oshnmnewutergapoyfshetsgiovodesthtzcaeorehiviseeoirncsmsrneelendaica,idbgrbnatlelti,iliotyittasytmltohiecihohotennrsaapenoyinasiozlsrmdlmtclteoycriomotemeosppiecirlpousmne,taolatrnehutitlisootthart.oseiaccrvagoneMddooboaisedrgdtnnlntasteiieittlooinabnyrrsbsfsncsaaaoiptyncemasessprrmuooentrteerip-adtrmbsosbeeiaclstemavtteaoittauhrmsiiesangcpvodieoeltoetaendr.edadsrnost[s.tbaei3ovatdwtnnMldra7noteeeagisg]nimshitfsmspenuhtoatatieisr-ascsrmiczbaneseectasiisisatuodnmp.dns.eisrgtHoridrTepeaisaovncdatcgmrotcvsereoieowwa.dneeensltreHmlcet.eirtndwhtiahiavo[phenegno3naeiwnrautedd7zasrshlt-s,]---. GGeeeett eett aall..[[4422]]iinnvveessttiiggaatteeddaanneewwccoolloorrcchhaannnneellmmeetthhooddttoorreemmoovvee aattmmoosspphheerricicssccaatttteerriningg, ,aannddoobbttaaiinneeddcchhaannnneellvvaalluueessaannddaattmmoosspphheerriciclliigghhtt..HHoowweevveerr, ,tthhiiss mmeetthhoodd ffooccuusseess oonn eennhhaanncciinngg tthhee qquuaalliittyy ooff tthhee ddeehhaazzeedd iimmaaggee uussiinngg aa dduuaall ttrraannssmmiissssiioonn mmaapp..CChhaannggeettaall.. The assessment of the dehazed images was evaluated and compared using the PIQE [47]
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