Real-world　stereo　image　super-resolution　has　a　significant　influence　on　enhancing　the　performance　of　computer　vision　systems.　Although　existing　methods　for　single-image　super-resolution　can　be　applied　to　enhance　stereo　images,　these　methods　often　introduce　notable　modifications　to　the　inherent　disparity,　resulting　in　a　loss　in　the　consistency　of　disparity　between　the　original　and　the　enhanced　stereo　images.　To　overcome　this　limitation,　this　paper　proposes　a　novel　approach　that　integrates　an　implicit　stereo　information　discriminator　and　a　hybrid　degradation　model.　This　combination　ensures　effective　enhancement　while　preserving　disparity　consistency.　The　proposed　method　bridges　the　gap　between　the　complex　degradations　in　real-world　stereo　domain　and　the　simpler　degradations　in　real-world　single-image　super-resolution　domain.　Our　results　demonstrate　impressive　performance　on　synthetic　and　real　datasets,　enhancing　visual　perception　while　maintaining　disparity　consistency.